Ricardo Andres Chavarriaga Lozano

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One fundamental limitation of EEG-based brain- computer interfaces is the time needed to calibrate the system prior to the detection of signals, due to the wide variety of issues affecting the EEG measurements. For event-related potentials (ERP), one of th ...

We describe error-related potentials generated while a human user monitors the performance of an external agent and discuss their use for a new type of Brain-Computer Interaction. In this approach, single trial detection of error-related EEG potentials is ...

The brain mechanism of embodiment in a virtual body has grown a scientific interest recently, with a particular focus on providing optimal virtual reality (VR) experiences. Disruptions from an embodied state to a less- or non-embodied state, denominated Br ...

Several studies describe evoked EEG potentials elicited when a subject is aware of an erroneous decision either taken by him or by an external interface. This paper try to detect Error-related potentials (ErrP) elicited when a human user want to monitors a ...

Objective. Event Related Potentials (ERPs) reflecting cognitive response to external stimuli, are widely used in brain computer interfaces. ERP waveforms are characterized by a series of components of particular latency and amplitude. The classical ERP dec ...

Real-time workload estimation can be an important tool to improve human-machine interaction. Despite multiple efforts in this sense, most studies focused on tasks designed to have extreme conditions of workload (high vs low), and these levels remain rather ...

One of the most popular methods in non-invasive brain machine interfaces (BMI) relies on the decoding of sensorimotor rhythms associated to sustained motor imagery. Although motor imagery has been intensively studied, its termination is mostly neglected. O ...

Background: One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low freque ...

Current non-invasive Brain Machine interfaces commonly rely on the decoding of sustained motor imagery activity. This approach enables a user to control brain-actuated devices by triggering predetermined motor actions. However, despite of its broad range o ...

One of the problems of non-invasive BCI applications is the noise and change of EEG recordings that can result in performance degradation. Signal noise and changes can for instance be the result of electrode misplacements, impedances degradation or complet ...

The prospect of controlling devices merely by the power of one’s thoughts is compelling, especially for assistive technology applications. In the accompanying video, we show how we have strived to push brain–computer interface (BCI) technology out of the l ...

Brain responses to visual stimuli can provide information about visual recognition processes. Several studies have shown stimulus-dependent modulation of the evoked neural responses after gaze shifts (i.e. eye fixation related potentials, EFRP) depending o ...

The exploitation of EEG signatures of cognitive processes can provide valuable information to improve interaction with brain actuated devices. In this work we study these correlates in a realistic situation simulated in a virtual reality environment. We fo ...

Anticipation increases the efficiency of daily tasks by partial advance activation of neural substrates involved in it. Previous off-line studies have shown the possibility of exploiting this activation for a Brain-Computer Interface (BCI) using electroenc ...

One important source of performance degradation in BCIs is bias towards one of the mental classes. Recent literature has focused on the general problem of classification accuracy drop, identifying non-stationarity as the generating factor, thus leading to ...

Practical brain-computer interfaces need to overcome several challenges, including tolerance to signal variability within- and across sessions. We introduce Robust Principal Component Analysis (RPCA) as a potential approach to tackle intra-trial variabilit ...

Intelligent cars exploit environmental information to support drivers by providing extra information and assisting complex maneouvers. They can also take into account the internal state of the driver by means of decoding cognition-related brain activity. H ...

This paper presents a new filter for EEG Event-Related Potentials that relies on spatio-temporal features. The results are analyzed with error-related potentials, and compared to the results obtained with Independent Component Analysis. Additionally, the o ...

Activity recognition systems based on body-worn motion sensors suffer from a decrease in performance during the deployment and run-time phases, because of probable changes in the sensors (e.g. displacement or rotatation), which is the case in many real-lif ...

Multi-stream automatic speech recognition (MS-ASR) has been confirmed to boost the recognition performance in noisy conditions. In this system, the generation and the fusion of the streams are the essential parts and need to be designed in such a way to re ...

Ricardo Andres Chavarriaga Lozano

Chat with Graph Search

Latency Correction of Error Potentials Between Different Experiments Reduces Calibration Time for Single-Trial Classification

Learning from EEG Error-related Potentials in Noninvasive Brain-Computer Interfaces

EEG signature of breaks in embodiment in VR

EEG Error-related Potentials Detection with a Bayesian Filter

Spatial covariance improves BCI performance for late ERPs components with high temporal variability

Analysis of EEG Correlates of Perceived Difficulty in Dynamically Changing Flying Tasks

User Adaptation to Closed-Loop Decoding of Motor Imagery Termination

Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques

Reliable decoding of motor state transitions during imagined movement

Toward online detection of BCI performance degradation

The Birth of the Brain–Controlled Wheelchair

EEG correlates of active visual search during simulated driving: An exploratory study

Anticipation- and Error-related EEG Signals during Realistic Human-Machine interaction: A Study on Visual and Tactile Feedback

Anticipation based Brain-Computer Interfacing (aBCI)

A supervised recalibration protocol for unbiased BCI

Using Robust Principal Component Analysis to Reduce EEG Intra-Trial Variability

Detecting Cognitive States for Enhancing Driving Experience

Spatio-temporal filtering for EEG error related potentials

Robust Activity Recognition Combining Anomaly Detection and Classifier Retraining

Stream fusion for multi-stream automatic speech recognition